A spectroscopy based prototype for the noninvasive detection of diabetes from human saliva using nanohybrids acting as nanozyme
Bibliographic record
Abstract
Abstract The recent prediction of diabetes to be a global pandemic invites a detection strategy preferably non-invasive, and bloodless to manage the disease and the associated complications. Here, we have synthesized chitosan polymer functionalized, organic–inorganic bio-compatible nano-hybrids of Mn3O4 nanoparticles, and characterized it by utilizing several optical methodologies for the structural characterization which shows the Michaelis Menten (MM) kinetics for glucose and alpha-amylase protein (well-known diabetes biomarkers). We have also studied the potentiality for the detection of alpha-amylase in human salivary secretion which is reported to be strongly correlated with uncontrolled hyperglycemia. Finally, we have developed a prototype for the measurement of glucose (LOD of 0.38 mg/dL, LOQ of 1.15 mg/dL) and HbA1c (LOD of 0.15% and LOQ of 0.45%) utilizing the basic knowledge in the study for the detection of uncontrolled hyperglycemia at the point-of-care. With the limited number of clinical trials, we have explored the potential of our work in combating the diabetic pandemic across the globe in near future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".